Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. Both hardware accelerated and parallel software-based NIDS solutions, based on commodity multi-core and GPU processors, have been proposed to overcome these challenges. Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. Both hardware accelerated and parallel software-based NIDS solutions, based on commodity multi-core and GPU processors, have been proposed to overcome these challenges. This work explores new parallel opportunities afforded by many-core processors for high performance, scalable and inexpensive NIDS. We exploit the huge many-core computational power by adopting a hybrid parallel architecture combining data and pipeline parallelism. We also design a hybrid load balancing scheme, using both ruleset and flow space partitioning. Furthermore, the proposed design leverages particular features of the processor to break the bottlenecks. We have integrated the open source NIDS Suricata into our proposed design and evaluated its performance with synthetic traffic. The prototype exhibits almost linear speedup and can handle up to 7.2 Gbps traffic with 100-bytes packets.
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